Naive Scene Graphs: How Visual is Modern Visual Relationship Detection?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern approaches to scene graph generation still struggle with their performance, with even state of the art approaches hovering under a 15% mean recall on certain evaluation modes. This poor performance is partially a result of networks heavily relying and fixating on non-visual data, such as class statistics, instead of the pixel-level signals present in the images. We demonstrate this by examining the 'visual-ness' of visual relationship detection approaches. We first describe and implement a new Naive Bayes-based statistical baseline for scene graph generation. Most notably, this basic classifier does not utilize the image pixels, but relies on the properties of the bounding boxes (class labels, topological configuration, … etc.) to predict the relationship labels. We demonstrate that our classical machine learning approach, one as simple as a categorical Naive Bayes classifier, can perform relationship detection in a manner that achieves relatively competitive performance to that of modern scene graph generators. This is an alarming finding regarding scene graph generation that implies that visual data in images may not be utilized in modern visual relationship detection past the point of object detection. We finally discuss how more visual modern approaches to scene graph generation appear to remedy some of these shortcomings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it